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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA
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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - nouveau livre

ISBN: 9783639288681

ID: 9783639288681

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. Textbooks New, Books~~Mathematics~~Probability & Statistics~~General, DIMENSIONALITY-REDUCTION-FOR-CLASSIFICATION-WITH-HIGH-DIMENSIONAL-DATA~~Siva-Tian, , , , , , , , , , VDM Verlag Dr. Mueller Akt.ges.&Co.KG

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Dimensionality Reduction For Classification with High-Dimensional Data - Siva Tian
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Siva Tian:
Dimensionality Reduction For Classification with High-Dimensional Data - nouveau livre

ISBN: 9783639288681

ID: 38988dd9bdc2ed105ad777fbb329b1dd

Dimensionality Reduction For Classification with High-Dimensional Data High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. Bücher / Fremdsprachige Bücher / Englische Bücher 978-3-639-28868-1, VDM Verlag

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Siva Tian
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Siva Tian:
DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - nouveau livre

ISBN: 9783639288681

ID: 94fc4019c62aa4d4a6941e8cf35a8ca0

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. Bücher / Naturwissenschaften, Medizin, Informatik & Technik / Mathematik / Stochastik & Statistik, [PU: VDM Verlag Dr. Müller, Saarbrücken]

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Siva Tian
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Siva Tian:
DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Livres de poche

2010, ISBN: 3639288688

ID: 9612496327

[EAN: 9783639288681], Neubuch, [PU: Vdm Verlag Aug 2010], This item is printed on demand - Print on Demand Titel. - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies. 124 pp. Englisch

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Dimensionality Reduction for Classification with High-Dimensional Data - Siva Tian
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Siva Tian:
Dimensionality Reduction for Classification with High-Dimensional Data - Livres de poche

ISBN: 9783639288681

Paperback, [PU: VDM Verlag], Probability & Statistics

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies.

Informations détaillées sur le livre - DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA


EAN (ISBN-13): 9783639288681
ISBN (ISBN-10): 3639288688
Livre de poche
Date de parution: 2010
Editeur: VDM Verlag Dr. Müller

Livre dans la base de données depuis 07.12.2009 03:44:47
Livre trouvé récemment le 06.07.2017 17:42:51
ISBN/EAN: 3639288688

ISBN - Autres types d'écriture:
3-639-28868-8, 978-3-639-28868-1


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